We evaluated our model on two semantic parsing datasets (ATIS and JOBS) used in [1]} dong2016language, where the input is a natural language sentence. The output of ATIS is in the \(\lambda \) -calculus form (illustrated in Figure REF ), while for JOBS, it is in the Prolog-style form. We used the standard train-dev-test split for the datasets [2]} zettlemoyer2012learning.
We see from the statistics in Table REF  that the logical forms for semantic parsing contain significantly fewer nodes and tokens than HS Python code.
